{"paper":{"title":"Adaptive Ensemble Learning of Spatiotemporal Processes with Calibrated Predictive Uncertainty: A Bayesian Nonparametric Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Brent A. Coull, Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou","submitted_at":"2019-04-01T01:03:57Z","abstract_excerpt":"Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assign to base models a set of deterministic, constant model weights that (1) do not fully account for individual models' varying accuracy across data subgroups, nor (2) provide uncertainty estimates for the ensemble prediction. These shortcomings can yield predictions that are precise but biased, which can negatively impact the performance of the algorithm in real-word applications. In this work, we present an adaptive, probabilistic approach to ensemble learning using a transformed Gaussian proc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00521","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}